A benchmark image dataset for industrial tools

Cai Luo, Leijian Yu, Erfu Yang, Huiyu Zhou, Peng Ren

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
17 Downloads (Pure)


Robots and Artificial Intelligence (AI) play an increasingly important role in manufacture. One of the tasks is to identify tools in the scene so that the tools can be applied to different assembly purposes. In the AI community, many datasets have been generated and deployed to train robots to recognize individual items, however, these datasets are scene-specific and lack generic background. In this paper, we report our dataset contains photos of 8 objects types that would be easily recognized by qualified workers. This is achieved by gathering images of common tools in a typical factory. The ground truth categories of our dataset are manually labeled by experienced workers, which would be worthy evaluation tools for the intelligence industrial systems. The equipment used and the image collection process are discussed, along with the data format. The mean average precisions range from 64.37% to 78.20%, which bring the possibility for future improvement. The dataset is ideal to evaluate and benchmark view-point variant, vision-based control algorithm for industry robots. It is now public available from
Original languageEnglish
Pages (from-to)341-348
Number of pages8
JournalPattern Recognition Letters
Early online date17 May 2019
Publication statusPublished - 1 Jul 2019


  • benchmark
  • industrial tools
  • image dataset


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